How long can the S&P 500 continue to rise after breaking through 7,600? An analysis of valuation bubble risks and market structure under AI-driven narratives

On June 1, 2026, the three major U.S. stock indices simultaneously hit record closing highs: the S&P 500 closed at 7,600.03 points, the Nasdaq Composite first surpassed the 27,000 mark at 27,086.81 points, and the Dow Jones Industrial Average closed at 51,079.37 points. The direct driver of this new high was the AI narrative—Nvidia announced the launch of the RTX Spark super chip to enter the PC market, with a single-day market value increase of about $319 billion, boosting the entire cloud computing service sector. This AI-driven valuation expansion has lasted over 18 months, but market disagreements over "bubble" versus "fundamental support" are deepening. The key judgment is: current valuation levels have not yet reached the extreme levels of the internet bubble period, but market concentration is at its highest in nearly 35 years, and whether AI capital expenditures can translate into sustainable profit growth from late 2026 to 2027 will be a critical variable in determining valuation trends. For the crypto market, the valuation re-rating pressure on the U.S. tech sector will transmit through liquidity preferences and risk sentiment to risk assets like Bitcoin and Ethereum, while macro factors such as the Federal Reserve's interest rate path and the dollar index also impose external constraints.

Market Background: Macro Drivers Behind the S&P 500 Breakthrough of 7,600 and Nasdaq 27,000

From a timeline perspective, this AI-driven U.S. stock rally can be roughly divided into three phases. The first phase is from late 2024 to the first half of 2025, when the market formed a preliminary consensus on the commercialization prospects of generative AI, with leading companies like Nvidia, Microsoft, and Meta continuously revising upward their capital expenditure guidance; the second phase is from the second half of 2025 to early 2026, when AI infrastructure investments moved from expectations to reality, with North American top five cloud service providers' quarterly capital expenditures growing over 50% year-over-year, and the semiconductor sector becoming a major influx of capital; the third phase, from March 2026 to now, saw the S&P 500 break previous highs and stabilize above 7,200 points, shifting market focus from hardware to application and inference layers, with Nvidia's RTX Spark super chip release seen as a key milestone in moving AI from data centers to PCs.

It is noteworthy that this new high occurred against a backdrop of a less accommodative macro environment. At the May 2026 Federal Reserve meeting, the federal funds rate target range was maintained at 5.25%-5.50%, and if June CPI data continues to exceed the 2% target, the timing of rate cuts could be further delayed until 2027. The U.S. 10-year Treasury yield re-approached 4.5% in late May, exerting ongoing pressure on growth stock valuations. In other words, the breakthrough of the S&P 500 and Nasdaq was not reliant on loose liquidity but driven by profit expectations within the AI industry itself. This "macro headwinds, industry tailwinds" structural feature is especially prominent in this cycle.

Valuation and Market Structure: Record Concentration, Capital Flows Toward AI Themes

Cross-sectional valuation data show that the forward P/E ratio of the S&P 500 is about 22.8x, with the premium of the S&P 500 Information Technology sector's forward P/E over the index at about 4%, the lowest since June 2020. The Nasdaq 100's forward P/E at the start of 2026 was approximately 27.44x. Compared to the Nasdaq 100's forward P/E exceeding 60x during the dot-com bubble in 2000, current levels are not yet in extreme bubble territory. However, a more notable change is in market structure: the Magnificent Seven stocks now account for about 34.8% of the S&P 500's total market cap, and over the six weeks ending May 15, the cap-weighted S&P 500 index outperformed the equal-weighted index by the largest margin in 35 years. This indicates that the breadth of this bull market is very limited, with about half of the S&P 500 components experiencing declines in 2026.

Fund flows also confirm this concentration. In April 2026, two major U.S. semiconductor ETFs received a combined net inflow of about $5.5 billion, setting a monthly record. Hedge funds bought tech stocks at their fastest pace in nearly three months in late May, with demand highly concentrated in AI-related chip companies. U.S. equity funds, after experiencing a continuous outflow of around $12 billion previously, turned to a net inflow of about $1.97 billion in early June, but the incremental capital mainly flowed into a few top AI stocks. Market behavior reflects a typical "capital crowding" structure rather than broad valuation re-rating.

Market Divergence: Bubble vs. Fundamentals and Valuation Paradigm Disputes

Currently, market views on the AI-driven rally fall into three clear lines of disagreement. The first, represented by legendary investors Michael Burry, Jim Rogers, and others, warns that Wall Street's current enthusiasm for AI is highly reminiscent of the blind optimism before the 2000 internet bubble. Supporting data include: the S&P 500 closed at a record high in May, but only a few AI-related components also hit new highs, a "index new high, broad stock participation lacking" feature nearly identical to the market structure before the 1999-2000 bubble peak.

The second line emphasizes fundamental support. Analysts expect the information technology sector's earnings per share (EPS) to grow 44% in Q1 2026, accounting for 87% of the total EPS growth of the S&P 500 during that period. Goldman Sachs estimates that just AI infrastructure investments will contribute about 40% of this year's S&P 500 earnings growth. Unlike the dot-com bubble, where many companies had only "concepts" without profits, the profitability of the Magnificent Seven has been validated over multiple quarters of financial reports. Some market participants argue that this tech rally is supported by fundamentals, but the high level of capital crowding is at an extreme historical level—these two are not mutually exclusive, as fundamentals do not preclude significant adjustments.

The third, intermediate stance, believes that traditional valuation methods have lost explanatory power for AI tech stocks. Methods such as P/E, P/S, discounted cash flow (DCF), growth rates, and industry narrative premiums coexist without a unified time scale or risk measure. The perpetual value assumption in traditional DCF models often accounts for over 80% of valuation results, but in the context of an immature AI industry with uncertain capital return cycles, this methodology's flaws are magnified. Even with the same information, different analysts may arrive at completely opposite conclusions due to valuation method differences.

Narrative Perspective: The Time Gap from Capital Expenditure to Profit Realization

The core logic of the AI narrative is: massive capital expenditure drives technological iteration, which in turn creates new application scenarios and revenue growth, ultimately forming a positive cash flow cycle. This logic was strongly supported by capital expenditure data from 2025 to early 2026—Magnificent Seven's capital expenditure growth in Q1 2026 reached 65% year-over-year, accounting for 33% of the total capital expenditure of S&P 500 component companies, with North American top five cloud service providers expected to invest up to $725 billion in 2026, up about 40% YoY.

However, the key vulnerability of this narrative lies in the time lag between capital expenditure and returns. While Nvidia, Dell, and other hardware suppliers are capturing early spending, the revenue from AI-driven products and services that can justify these expenditures has yet to materialize at scale. Currently, the market's clear validation of AI revenue sources remains concentrated in infrastructure (computing power leasing, model training services) and a few enterprise applications (code generation, customer service automation), with killer consumer applications still absent. Some analysts suggest 2026 may be the peak of AI capital expenditure growth, and based on the profits and cash flows of the four major U.S. cloud providers, whether this growth can be sustained into 2027 is highly uncertain. Power supply constraints, public approval of data center land use, and the ability of top tech companies to sustain earnings will be three core variables determining the sustainability of AI investments.

Industry Impact: Differentiation within the AI Supply Chain and Capital Siphoning Effect

This AI-driven valuation expansion is not a broad-based rally but shows significant structural differentiation within the industry chain. The Philadelphia Semiconductor Index rose about 64% in 2026, while the S&P 500 increased only about 9%. The gains in semiconductor equipment, AI acceleration chips, and data center infrastructure—"selling shovels"—far outpaced those in application and software services. This internal industry chain differentiation reflects market preferences for commercialization pathways: hardware spending is realized first, followed by software revenue and application profits.

For the broader capital markets, the siphoning effect of AI themes has begun to signal structural adjustments. On one hand, some public funds' net asset values and holdings have diverged significantly, implying fund managers are reducing tech positions; on the other hand, the performance of equal-weighted S&P 500 and value stocks has recently outperformed the traditional cap-weighted S&P 500, with more than half of the Magnificent Seven components showing divergence. Historically, this process of "funds reallocating from top stocks to rebalancing" often precedes valuation pressure relief.

In the crypto market, the correlation between Bitcoin (BTC), Ethereum, and the Nasdaq's 90-day rolling correlation coefficient has risen above 0.65 in Q1 2026. If AI valuations face correction pressure in late 2026, risk appetite contraction could transmit to crypto assets via two channels: first, institutional capital withdrawing from risk assets overall; second, a strengthening dollar index exerting downward pressure on dollar-denominated crypto assets. On the macro level, the market has partially priced in the Fed's delay in rate cuts via the federal funds futures, but if June CPI data continues to surprise on the high side, further increases in real interest rates could pose systemic pressure on all growth assets.

Conclusion

The S&P 500 surpassing 7,600 and Nasdaq breaking 27,000 are phase results of the AI narrative-driven valuation expansion, not the end point. The core current assessment is: valuations are high historically but not yet extreme; the biggest structural concern is market concentration—about 35% of market cap is held by seven stocks, and the gap between equal-weighted and cap-weighted indices has reached its widest in 35 years. The mid-term trend depends on two variables: whether AI capital expenditures can translate into sustainable profit growth from late 2026 to 2027, and whether changes in the Fed's rate cut path will alter the current valuation framework. Investors should focus on the revenue growth of cloud service providers' AI businesses, the impact of June CPI data on rate cut expectations, and the relative performance of equal-weighted versus cap-weighted S&P 500, as these indicators will provide earlier signals than index levels.

FAQ

Is the current valuation level of the S&P 500 already in bubble territory?

The current forward P/E of the S&P 500 is about 22.8x, and the Nasdaq 100's forward P/E is about 27.44x, still below the extreme levels seen during the 2000 internet bubble.

How long can the high growth in AI capital expenditures last?

2026 may be the peak of this cycle's capital expenditure growth; its sustainability into 2027 depends on power supply, data center approval processes, and the profit realization of leading tech companies.

What does the high market concentration mean?

The Magnificent Seven stocks account for about 34.8% of the S&P 500's market cap, and the performance gap between equal-weighted and cap-weighted indices has reached its widest in 35 years, meaning the index's new highs mask declines in most individual stocks.

How will the Fed's delay in rate cuts affect AI tech stock valuations?

High interest rates will compress growth stocks' valuation multiples; for every 100 basis point increase in the 10-year Treasury yield, the Nasdaq 100's forward P/E could shrink by 10%-15%.

How does the AI narrative relate to the crypto market?

The 90-day rolling correlation between Bitcoin and Nasdaq rose above 0.65 in Q1 2026, and a synchronized risk appetite contraction could transmit via institutional fund withdrawals and dollar strengthening.

If AI valuations adjust, how much impact will crypto assets face?

Historical data shows that a more than 10% correction in the Nasdaq often coincides with a 15%-25% pullback in Bitcoin, but the magnitude depends on whether the correction is driven by liquidity or industry risks.

What are the most important indicators to watch in late 2026?

Three key indicators are: the revenue growth of major cloud providers' AI businesses, the impact of June CPI data on rate cut expectations, and the relative performance of equal-weighted versus cap-weighted S&P 500.

How should ordinary investors respond to the current high valuation environment?

Pay attention to concentration risks, avoid overexposure to a single AI theme, and closely monitor the validation of capital expenditure returns (from Q4 2026 to Q2 2027).